One can test visually by plotting an empirical ACF, as seen in Figure REF  (A). For fixed values of \(k\) , one can test the value of ACF at \(k\)  using asymptotic normality. Finally, there exist combined (portmanteau) test for empirical ACF values with lags \(k = 1, \ldots , K\)  for fixed \(K\) . The most classic example is the Box-Pierce test, see [1]}: sum of squares of empirical ACF values. It follows from (REF ) that after normalization, this sum converges weakly to the \(\chi ^2_k\)  distribution, as \(N \rightarrow \infty \) . This allows us to test the white noise hypothesis. A more precise modification, the Ljung-Box test, was developed in [2]}, see also [3]}. Such autocorrelation tests are good in distinguishing white noise from linear autoregressive models, for example AR(1) or MA(1):
\(X_t - b = a(X_{t-1} - b) + Z_t,\quad X_t = Z_t + aZ_{t-1},\) 
